In [1]:
#Deep Q Network for ENO
#Resetting the battery to BOPT on each day during training
#Increase no. of actions to 10
In [2]:
%matplotlib inline
In [3]:
import pulp
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

import pandas as pd
import numpy as np
from random import shuffle
from mpl_toolkits.mplot3d import Axes3D

import torch
import torch.nn as nn
import torch.nn.functional as F
In [4]:
np.random.seed(230228)
In [5]:
# Hyper Parameters
BATCH_SIZE = 24
LR = 0.01                   # learning rate
EPSILON = 0.9               # greedy policy
GAMMA = 0.9                 # reward discount
LAMBDA = 0.8                # parameter decay
TARGET_REPLACE_ITER = 24*7*4*2    # target update frequency (every month)
MEMORY_CAPACITY = 24*7*4*6      # store upto four month worth of memory   

N_ACTIONS = 10 #no. of duty cycles (0,1,2,3,4)
N_STATES = 4 #number of state space parameter [batt, enp, henergy, fcast]
HIDDEN_LAYER = 30
In [6]:
class ENO(object):
    def __init__(self, year=2010):
        self.year = year
        self.day = None
        self.hr = None
        
        self.TIME_STEPS = None
        self.NO_OF_DAYS = None
        
        self.BMIN = 0.0
        self.BMAX = 20000.0 #Battery capacity
        self.BOPT = 0.6 * self.BMAX #Assuming 60% of battery is the optimal level
        self.HMAX = 1000
        
        self.senergy = None #matrix with harvested energy data for the entire year
        self.fforecast = None #matrix with forecast values for each day
        
        self.batt = None #battery variable
        self.enp = None #enp at end of hr
        self.henergy = None #harvested energy variable
        self.fcast = None #forecast variable
    
    #function to map total day energy into day_state
    def get_day_state(self,tot_day_energy):
        if (tot_day_energy < 2500):
            day_state = 0
        elif (2500 <= tot_day_energy < 5000):
            day_state = 1
        elif (5000 <= tot_day_energy < 8000):
            day_state = 2
        elif (8000 <= tot_day_energy < 10000):
            day_state = 3
        elif (10000 <= tot_day_energy < 12000):
            day_state = 4
        else:
            day_state = 5
        return int(day_state)

    #function to get the solar data for the given year and prep it
    def get_data(self):
        filename = str(self.year)+'.csv'
        #skiprows=4 to remove unnecessary title texts
        #usecols=4 to read only the Global Solar Radiation (GSR) values
        solar_radiation = pd.read_csv(filename, skiprows=4, encoding='shift_jisx0213', usecols=[4])
        
        #convert dataframe to numpy array
        solar_radiation = solar_radiation.values
        solar_energy = np.array([i *0.0165*1000000*0.15*1000/(60*60) for i in solar_radiation])
        
        #reshape solar_energy into no_of_daysx24 array
        _senergy = solar_energy.reshape(-1,24)
        _senergy[np.isnan(_senergy)] = 0 #convert missing data in CSV files to zero
        self.senergy = _senergy
        
        
        #create a perfect forecaster
        tot_day_energy = np.sum(_senergy, axis=1) #contains total energy harvested on each day
        get_day_state = np.vectorize(self.get_day_state)
        self.fforecast = get_day_state(tot_day_energy)
        
        return 0
    
    def reset(self):
        
        self.get_data() #first get data for the given year
        
        self.TIME_STEPS = self.senergy.shape[1]
        self.NO_OF_DAYS = self.senergy.shape[0]
        
        print("Environment is RESET")
        
        self.day = 0
        self.hr = 0
        
        self.batt = self.BOPT #battery returns to optimal level
        self.enp = self.BOPT - self.batt #enp is reset to zero
        self.henergy = self.senergy[self.day][self.hr] 
        self.fcast = self.fforecast[self.day]
        
        state = [self.batt/self.BMAX, self.enp/(self.BMAX/2), self.henergy/self.HMAX, self.fcast/5] #normalizing all state values within [0,1] interval
        reward = 0
        done = False
        info = "RESET"
        return [state, reward, done, info]
    
    
    #reward function
    def rewardfn(self):
        mu = 0
        sig = 1000
#         return ((1./(np.sqrt(2.*np.pi)*sig)*np.exp(-np.power((self.enp - mu)/sig, 2.)/2)) * 2000000)-400


        if(np.abs(self.enp) <= 2400): #24hr * 100mW/hr
            return ((1./(np.sqrt(2.*np.pi)*sig)*np.exp(-np.power((self.enp - mu)/sig, 2.)/2)) * 1000000)
        else:
            return -100 - 0.05*np.abs(self.enp)
    
    def step(self, action):
        done = False
        info = "OK"
#         print("Next STEP")
        
        reward = 0
        e_consumed = (action+1)*50
        
        self.batt += (self.henergy - e_consumed)
        self.batt = np.clip(self.batt, self.BMIN, self.BMAX)
        self.enp = self.BOPT - self.batt
        
        if(self.hr < self.TIME_STEPS - 1):
            self.hr += 1
            self.henergy = self.senergy[self.day][self.hr] 
        else:
            if(self.day < self.NO_OF_DAYS -1):
                reward = self.rewardfn() #give reward only at the end of the day
                self.hr = 0
                self.day += 1
                self.henergy = self.senergy[self.day][self.hr] 
                self.fcast = self.fforecast[self.day]
            else:
                reward = self.rewardfn()
                done = True
                info = "End of the year"
                
        _state = [self.batt/self.BMAX, self.enp/(self.BMAX/2), self.henergy/self.HMAX, self.fcast/5]
        return [_state, reward, done, info]
In [7]:
class Net(nn.Module):
    def __init__(self, ):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(N_STATES, HIDDEN_LAYER)
        self.fc1.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc2 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc2.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc3 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc3.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc4 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc4.weight.data.normal_(0, 0.1)   # initialization
        
        self.out = nn.Linear(HIDDEN_LAYER, N_ACTIONS)
        self.out.weight.data.normal_(0, 0.1)   # initialization

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        actions_value = self.out(x)
        return actions_value
In [8]:
class DQN(object):
    def __init__(self):
        self.eval_net, self.target_net = Net(), Net()
        print("Neural net")
        print(self.eval_net)

        self.learn_step_counter = 0                                     # for target updating
        self.memory_counter = 0                                         # for storing memory
        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory [mem: ([s], a, r, [s_]) ]
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
        self.loss_func = nn.MSELoss()

    def choose_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
        if np.random.uniform() < EPSILON:   # greedy
            actions_value = self.eval_net.forward(x)
            action = torch.max(actions_value, 1)[1].data.numpy()
            action = action[0] # return the argmax index
        else:   # random
            action = np.random.randint(0, N_ACTIONS)
            action = action
        return action
    
    def choose_greedy_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
    
        actions_value = self.eval_net.forward(x)
        action = torch.max(actions_value, 1)[1].data.numpy()
        action = action[0] # return the argmax index

        return action

    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, [a, r], s_))
        # replace the old memory with new memory
        index = self.memory_counter % MEMORY_CAPACITY
        self.memory[index, :] = transition
        self.memory_counter += 1

    def learn(self):
        # target parameter update
        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:
            self.target_net.load_state_dict(self.eval_net.state_dict())
        self.learn_step_counter += 1

        # sample batch transitions
        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
        b_memory = self.memory[sample_index, :]
        b_s = torch.FloatTensor(b_memory[:, :N_STATES])
        b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
        b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
        b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])

        # q_eval w.r.t the action in experience
        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)
        q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate
        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)
        loss = self.loss_func(q_eval, q_target)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
In [9]:
dqn = DQN()
eno = ENO(2010)
NO_OF_ITERATIONS = 30
avg_reward_rec = np.empty(1)
for iteration in range(NO_OF_ITERATIONS):
    print('\nCollecting experience... Iteration:', iteration)
    s, r, done, info = eno.reset()
    record = np.empty(4)

    while True:
    #     print([eno.day, eno.hr])

        a = dqn.choose_action(s)
    #     print("Actin is ",a)
        #state = [batt, enp, henergy, fcast]
        record = np.vstack((record, [s[0],s[2],r, a])) #record battery, henergy, reward and action
    #     print("Action is" , a)
        # take action
        s_, r, done, info = eno.step(a)
    #     print([s_,r])
    #     print("\n")
#         if eno.hr == 0:
#             eno.batt = eno.BOPT #resetting the battery to the optimal value for each day
        dqn.store_transition(s, a, r, s_)

        if dqn.memory_counter > MEMORY_CAPACITY:
            dqn.learn()

        if done:
            print("End of Data")
            break

        s = s_

    record = np.delete(record, 0, 0) #remove the first row which is garbage

    reward_rec = record[:,2]
    reward_rec = reward_rec[reward_rec != 0]
    print("Average reward =", np.mean(reward_rec) )
    avg_reward_rec = np.append(avg_reward_rec, np.mean(reward_rec))

    action_rec = record[:,3]

    fig = plt.figure(figsize=(10,5))

    ax1 = fig.add_subplot(1,2,1)
    ax1.plot(reward_rec,'y')
    plt.ylabel("REWARD")
    plt.xlabel("Day")
    ax1.set_ylim([-400,400])

    ax2 = fig.add_subplot(1,2,2)
    plt.hist(action_rec, rwidth=0.75)#     plt.ylabel("Action")

    fig.tight_layout()
    plt.show()

avg_reward_rec = np.delete(avg_reward_rec, 0, 0) #remove the first row which is garbage
plt.plot(avg_reward_rec,'b')

Neural net
Net(
  (fc1): Linear(in_features=4, out_features=30, bias=True)
  (fc2): Linear(in_features=30, out_features=30, bias=True)
  (fc3): Linear(in_features=30, out_features=30, bias=True)
  (fc4): Linear(in_features=30, out_features=30, bias=True)
  (out): Linear(in_features=30, out_features=10, bias=True)
)

Collecting experience... Iteration: 0
Environment is RESET
End of Data
Average reward = -417.62942015025607

Collecting experience... Iteration: 1
Environment is RESET
End of Data
Average reward = -251.35315931454917

Collecting experience... Iteration: 2
Environment is RESET
End of Data
Average reward = -246.64528657420794

Collecting experience... Iteration: 3
Environment is RESET
End of Data
Average reward = -232.98143000403093

Collecting experience... Iteration: 4
Environment is RESET
End of Data
Average reward = -206.29079024679808

Collecting experience... Iteration: 5
Environment is RESET
End of Data
Average reward = -229.8599668216744

Collecting experience... Iteration: 6
Environment is RESET
End of Data
Average reward = -265.53423441829267

Collecting experience... Iteration: 7
Environment is RESET
End of Data
Average reward = -206.9801251962834

Collecting experience... Iteration: 8
Environment is RESET
End of Data
Average reward = -250.68055641826857

Collecting experience... Iteration: 9
Environment is RESET
End of Data
Average reward = -233.09239568692314

Collecting experience... Iteration: 10
Environment is RESET
End of Data
Average reward = -211.51670212561666

Collecting experience... Iteration: 11
Environment is RESET
End of Data
Average reward = -225.9019988340635

Collecting experience... Iteration: 12
Environment is RESET
End of Data
Average reward = -301.6924867128953

Collecting experience... Iteration: 13
Environment is RESET
End of Data
Average reward = -214.65261632776634

Collecting experience... Iteration: 14
Environment is RESET
End of Data
Average reward = -233.76327347185693

Collecting experience... Iteration: 15
Environment is RESET
End of Data
Average reward = -251.8517718160005

Collecting experience... Iteration: 16
Environment is RESET
End of Data
Average reward = -324.1399120411473

Collecting experience... Iteration: 17
Environment is RESET
End of Data
Average reward = -268.74548578472115

Collecting experience... Iteration: 18
Environment is RESET
End of Data
Average reward = -237.25462123992511

Collecting experience... Iteration: 19
Environment is RESET
End of Data
Average reward = -217.89858068281725

Collecting experience... Iteration: 20
Environment is RESET
End of Data
Average reward = -202.34784888291532

Collecting experience... Iteration: 21
Environment is RESET
End of Data
Average reward = -218.60973259282244

Collecting experience... Iteration: 22
Environment is RESET
End of Data
Average reward = -203.98777007674312

Collecting experience... Iteration: 23
Environment is RESET
End of Data
Average reward = -170.36032449553932

Collecting experience... Iteration: 24
Environment is RESET
End of Data
Average reward = -179.43286146128818

Collecting experience... Iteration: 25
Environment is RESET
End of Data
Average reward = -239.13970844700214

Collecting experience... Iteration: 26
Environment is RESET
End of Data
Average reward = -228.09406391715106

Collecting experience... Iteration: 27
Environment is RESET
End of Data
Average reward = -245.3292093079449

Collecting experience... Iteration: 28
Environment is RESET
End of Data
Average reward = -196.06351745376023

Collecting experience... Iteration: 29
Environment is RESET
End of Data
Average reward = -227.4074249792907
Out[9]:
[<matplotlib.lines.Line2D at 0x7f75641c17f0>]
In [10]:
print('\nTesting...')
s, r, done, info = eno.reset()
test_record = np.empty(4)

while True:
#     print([eno.day, eno.hr])

    a = dqn.choose_greedy_action(s)
    
    #state = [batt, enp, henergy, fcast]
    test_record = np.vstack((test_record, [s[0],s[2],r, a])) #record battery, henergy, reward and action
#     print("Action is" , a)
    # take action
    s_, r, done, info = eno.step(a)
#     print([s_,r])
#     print("\n")
#     if eno.hr == 0:
#         eno.batt = eno.BOPT #resetting the battery to the optimal value for each day
   
    if done:
        print("End of Data")
        break
       
    s = s_

Testing...
Environment is RESET
End of Data
In [11]:
test_reward_rec = test_record[:,2]
test_reward_rec = test_reward_rec[test_reward_rec != 0]
plt.plot(test_reward_rec)
Out[11]:
[<matplotlib.lines.Line2D at 0x7f7564407c18>]
In [12]:
plt.plot(test_record[:,0],'r')
Out[12]:
[<matplotlib.lines.Line2D at 0x7f756416bef0>]
In [13]:
#Average Battery Percentage
np.mean(test_record[:,0])
Out[13]:
0.7910507576189932
In [14]:
for DAY in range(eno.NO_OF_DAYS):
    START = DAY*24
    END = START+24

    fig = plt.figure(figsize=(10,4))
    st = fig.suptitle("DAY %s" %(DAY))

    ax1 = fig.add_subplot(141)
    ax1.plot(test_record[START:END,0])
    ax1.set_title("Battery")
    ax1.set_ylim([0,1])

    ax2 = fig.add_subplot(142)
    ax2.plot(test_record[START:END,1])
    ax2.set_title("Harvested Energy")
    ax2.set_ylim([0,1])

    ax3 = fig.add_subplot(144)
    ax3.axis('off')
    if END < (eno.NO_OF_DAYS*eno.TIME_STEPS):
        plt.text(0.5, 0.5, "REWARD = %.2f\n" %(test_record[END+1,2]),fontsize=14, ha='center')

    ax4 = fig.add_subplot(143)
    ax4.plot(test_record[START:END,3])
    ax4.set_title("Action")
    ax4.set_ylim([0,N_ACTIONS])

    fig.tight_layout()
    st.set_y(0.95)
    fig.subplots_adjust(top=0.75)
    plt.show()


AttributeErrorTraceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py in amin(a, axis, out, keepdims)
   2412         try:
-> 2413             amin = a.min
   2414         except AttributeError:

AttributeError: 'list' object has no attribute 'min'

During handling of the above exception, another exception occurred:

KeyboardInterruptTraceback (most recent call last)
<ipython-input-14-1f594b071564> in <module>()
     29     st.set_y(0.95)
     30     fig.subplots_adjust(top=0.75)
---> 31     plt.show()

/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py in show(*args, **kw)
    251     """
    252     global _show
--> 253     return _show(*args, **kw)
    254 
    255 

/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py in show(close, block)
     34     try:
     35         for figure_manager in Gcf.get_all_fig_managers():
---> 36             display(figure_manager.canvas.figure)
     37     finally:
     38         show._to_draw = []

/usr/local/lib/python3.6/dist-packages/IPython/core/display.py in display(include, exclude, metadata, transient, display_id, *objs, **kwargs)
    296             publish_display_data(data=obj, metadata=metadata, **kwargs)
    297         else:
--> 298             format_dict, md_dict = format(obj, include=include, exclude=exclude)
    299             if not format_dict:
    300                 # nothing to display (e.g. _ipython_display_ took over)

/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
    178             md = None
    179             try:
--> 180                 data = formatter(obj)
    181             except:
    182                 # FIXME: log the exception

<decorator-gen-9> in __call__(self, obj)

/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
    222     """show traceback on failed format call"""
    223     try:
--> 224         r = method(self, *args, **kwargs)
    225     except NotImplementedError:
    226         # don't warn on NotImplementedErrors

/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py in <lambda>(fig)
    239 
    240     if 'png' in formats:
--> 241         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    242     if 'retina' in formats or 'png2x' in formats:
    243         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    123 
    124     bytes_io = BytesIO()
--> 125     fig.canvas.print_figure(bytes_io, **kw)
    126     data = bytes_io.getvalue()
    127     if fmt == 'svg':

/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   2210                     orientation=orientation,
   2211                     dryrun=True,
-> 2212                     **kwargs)
   2213                 renderer = self.figure._cachedRenderer
   2214                 bbox_inches = self.figure.get_tightbbox(renderer)

/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
    511 
    512     def print_png(self, filename_or_obj, *args, **kwargs):
--> 513         FigureCanvasAgg.draw(self)
    514         renderer = self.get_renderer()
    515         original_dpi = renderer.dpi

/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py in draw(self)
    431             # if toolbar:
    432             #     toolbar.set_cursor(cursors.WAIT)
--> 433             self.figure.draw(self.renderer)
    434             # A GUI class may be need to update a window using this draw, so
    435             # don't forget to call the superclass.

/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py in draw(self, renderer)
   1473 
   1474             mimage._draw_list_compositing_images(
-> 1475                 renderer, self, artists, self.suppressComposite)
   1476 
   1477             renderer.close_group('figure')

/usr/local/lib/python3.6/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    139     if not_composite or not has_images:
    140         for a in artists:
--> 141             a.draw(renderer)
    142     else:
    143         # Composite any adjacent images together

/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
   2605             renderer.stop_rasterizing()
   2606 
-> 2607         mimage._draw_list_compositing_images(renderer, self, artists)
   2608 
   2609         renderer.close_group('axes')

/usr/local/lib/python3.6/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    139     if not_composite or not has_images:
    140         for a in artists:
--> 141             a.draw(renderer)
    142     else:
    143         # Composite any adjacent images together

/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in draw(self, renderer, *args, **kwargs)
   1200         # the actual bbox
   1201 
-> 1202         self._update_label_position(renderer)
   1203 
   1204         self.label.draw(renderer)

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in _update_label_position(self, renderer)
   2284                 # use axes if spine doesn't exist
   2285                 spinebbox = self.axes.bbox
-> 2286             bbox = mtransforms.Bbox.union(bboxes + [spinebbox])
   2287             left = bbox.x0
   2288             self.label.set_position(

/usr/local/lib/python3.6/dist-packages/matplotlib/transforms.py in union(bboxes)
    745         if not len(bboxes):
    746             raise ValueError("'bboxes' cannot be empty")
--> 747         x0 = np.min([bbox.xmin for bbox in bboxes])
    748         x1 = np.max([bbox.xmax for bbox in bboxes])
    749         y0 = np.min([bbox.ymin for bbox in bboxes])

/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py in amin(a, axis, out, keepdims)
   2411     if type(a) is not mu.ndarray:
   2412         try:
-> 2413             amin = a.min
   2414         except AttributeError:
   2415             pass

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